2/21/23
Q: I’m curious about how we would collaborate for the case study as a group with a single file - would it be a bunch of pushes and pulls or is it a little more complicated than that?
A: As long as you’re working on separate lines/parts of the file, you can all push and pull to the same file! So as long as tasks are well delineated and you always remember to pull before you get started (and nobody pushes while you’re working on your part), there won’t be any issues. However, if you’re all working on similar parts/pushing and pulling at the same time, you will run into merge conflicts. These can certainly be handled viagit/GitHub but make things a tad more complicated. For those who are less comfortable using GitHub, some groups choose to work in separate .Rmd files, pushing those to your group repo, and then combine them all at the end! This is very much something for your group to discuss!
Due Dates:
Notes:
Rows: 1,364
Columns: 50
$ YEAR <dbl> 1980, 1980, 1980, 1980, 1980, 1980, 198…
$ STATE <chr> "Alaska", "Arizona", "Arkansas", "Calif…
$ Black_Female_10_to_19_years <dbl> 0.26391223, 0.28748026, 1.81933049, 0.7…
$ Black_Female_20_to_29_years <dbl> 0.44331324, 0.27753816, 1.50296508, 0.8…
$ Black_Female_30_to_39_years <dbl> 0.201146585, 0.165433651, 0.842359498, …
$ Black_Female_40_to_49_years <dbl> 0.115646931, 0.119305223, 0.633866784, …
$ Black_Female_50_to_64_years <dbl> 0.092418701, 0.136484590, 1.015244173, …
$ Black_Female_65_years_and_over <dbl> 0.026440644, 0.103332066, 1.156103458, …
$ Black_Male_10_to_19_years <dbl> 0.29677770, 0.31145827, 1.81159721, 0.8…
$ Black_Male_20_to_29_years <dbl> 0.69462291, 0.33792181, 1.26270912, 0.8…
$ Black_Male_30_to_39_years <dbl> 0.29875457, 0.18879028, 0.71111220, 0.5…
$ Black_Male_40_to_49_years <dbl> 0.147771078, 0.127310077, 0.476448668, …
$ Black_Male_50_to_64_years <dbl> 0.102797272, 0.130636295, 0.741127809, …
$ Black_Male_65_years_and_over <dbl> 0.027181971, 0.085421662, 0.870583784, …
$ Other_Female_10_to_19_years <dbl> 2.04383711, 0.80253231, 0.06531781, 0.5…
$ Other_Female_20_to_29_years <dbl> 1.76559257, 0.65515527, 0.07942996, 0.6…
$ Other_Female_30_to_39_years <dbl> 1.24839379, 0.44180215, 0.06702176, 0.6…
$ Other_Female_40_to_49_years <dbl> 0.79124246, 0.31098310, 0.04216167, 0.3…
$ Other_Female_50_to_64_years <dbl> 0.74651577, 0.28875958, 0.04390930, 0.4…
$ Other_Female_65_years_and_over <dbl> 0.37906494, 0.16250950, 0.03158848, 0.2…
$ Other_Male_10_to_19_years <dbl> 2.15157655, 0.81174338, 0.07034226, 0.5…
$ Other_Male_20_to_29_years <dbl> 1.76361570, 0.59561232, 0.07497349, 0.6…
$ Other_Male_30_to_39_years <dbl> 1.19971335, 0.38931370, 0.04928327, 0.5…
$ Other_Male_40_to_49_years <dbl> 0.79519620, 0.25710568, 0.03552066, 0.3…
$ Other_Male_50_to_64_years <dbl> 0.74058515, 0.23513802, 0.03281182, 0.3…
$ Other_Male_65_years_and_over <dbl> 0.393397252, 0.150630154, 0.019486117, …
$ White_Female_10_to_19_years <dbl> 6.121874, 7.373713, 6.669014, 6.720429,…
$ White_Female_20_to_29_years <dbl> 8.608777, 8.195326, 6.657261, 7.997032,…
$ White_Female_30_to_39_years <dbl> 7.054710, 6.259248, 5.710656, 6.373367,…
$ White_Female_40_to_49_years <dbl> 3.749629, 4.414842, 4.319801, 4.342865,…
$ White_Female_50_to_64_years <dbl> 3.352525, 7.079325, 6.767843, 6.587129,…
$ White_Female_65_years_and_over <dbl> 1.048977, 6.082958, 6.700472, 5.556054,…
$ White_Male_10_to_19_years <dbl> 6.873085, 7.641858, 6.993288, 7.029783,…
$ White_Male_20_to_29_years <dbl> 9.804784, 8.406997, 6.564418, 8.471549,…
$ White_Male_30_to_39_years <dbl> 8.483740, 6.285382, 5.560709, 6.519398,…
$ White_Male_40_to_49_years <dbl> 4.666650, 4.336730, 4.170641, 4.353268,…
$ White_Male_50_to_64_years <dbl> 4.103242, 6.210707, 5.993248, 6.065005,…
$ White_Male_65_years_and_over <dbl> 1.020807, 4.797064, 4.924526, 3.754192,…
$ Unemployment_rate <dbl> 9.6, 6.6, 7.6, 6.8, 5.8, 7.6, 7.4, 6.1,…
$ Poverty_rate <dbl> 9.6, 12.8, 21.5, 11.0, 8.6, 11.8, 20.9,…
$ Viol_crime_count <dbl> 1919, 17673, 7656, 210290, 15215, 2824,…
$ Population <dbl> 404680, 2735840, 2288809, 23792840, 290…
$ police_per_100k_lag <dbl> 194.72176, 262.66156, 152.00045, 243.92…
$ RTC_LAW_YEAR <dbl> 1995, 1995, 1996, Inf, 2003, Inf, Inf, …
$ RTC_LAW <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALS…
$ TIME_0 <dbl> 1980, 1980, 1980, 1980, 1980, 1980, 198…
$ TIME_INF <dbl> 2010, 2010, 2010, 2010, 2010, 2010, 201…
$ Viol_crime_rate_1k <dbl> 4.742018, 6.459808, 3.344971, 8.838373,…
$ Viol_crime_rate_1k_log <dbl> 1.5564629, 1.8655995, 1.2074581, 2.1791…
$ Population_log <dbl> 12.91085, 14.82195, 14.64354, 16.98490,…
| Name | LOTT_DF |
| Number of rows | 1364 |
| Number of columns | 50 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| logical | 1 |
| numeric | 48 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| STATE | 0 | 1 | 4 | 20 | 0 | 44 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| RTC_LAW | 0 | 1 | 0.36 | FAL: 868, TRU: 496 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| YEAR | 0 | 1 | 1995.00 | 8.95 | 1980.00 | 1987.00 | 1995.00 | 2003.00 | 2010.00 | ▇▇▇▇▇ |
| Black_Female_10_to_19_years | 0 | 1 | 1.02 | 1.02 | 0.02 | 0.26 | 0.64 | 1.44 | 6.53 | ▇▂▁▁▁ |
| Black_Female_20_to_29_years | 0 | 1 | 1.01 | 1.09 | 0.02 | 0.26 | 0.61 | 1.37 | 7.73 | ▇▂▁▁▁ |
| Black_Female_30_to_39_years | 0 | 1 | 0.93 | 1.00 | 0.01 | 0.21 | 0.58 | 1.29 | 6.11 | ▇▂▁▁▁ |
| Black_Female_40_to_49_years | 0 | 1 | 0.76 | 0.87 | 0.01 | 0.14 | 0.49 | 1.10 | 5.45 | ▇▂▁▁▁ |
| Black_Female_50_to_64_years | 0 | 1 | 0.78 | 0.97 | 0.00 | 0.14 | 0.45 | 1.08 | 6.10 | ▇▂▁▁▁ |
| Black_Female_65_years_and_over | 0 | 1 | 0.62 | 0.86 | 0.00 | 0.08 | 0.35 | 0.82 | 6.12 | ▇▁▁▁▁ |
| Black_Male_10_to_19_years | 0 | 1 | 1.04 | 1.02 | 0.03 | 0.29 | 0.68 | 1.47 | 6.32 | ▇▃▁▁▁ |
| Black_Male_20_to_29_years | 0 | 1 | 0.95 | 0.93 | 0.04 | 0.31 | 0.66 | 1.25 | 6.57 | ▇▂▁▁▁ |
| Black_Male_30_to_39_years | 0 | 1 | 0.82 | 0.84 | 0.02 | 0.24 | 0.55 | 1.10 | 5.37 | ▇▂▁▁▁ |
| Black_Male_40_to_49_years | 0 | 1 | 0.66 | 0.72 | 0.01 | 0.16 | 0.44 | 0.93 | 4.45 | ▇▂▁▁▁ |
| Black_Male_50_to_64_years | 0 | 1 | 0.64 | 0.76 | 0.00 | 0.14 | 0.40 | 0.87 | 4.79 | ▇▂▁▁▁ |
| Black_Male_65_years_and_over | 0 | 1 | 0.39 | 0.51 | 0.00 | 0.06 | 0.24 | 0.52 | 3.56 | ▇▁▁▁▁ |
| Other_Female_10_to_19_years | 0 | 1 | 0.51 | 0.78 | 0.03 | 0.15 | 0.27 | 0.56 | 5.33 | ▇▁▁▁▁ |
| Other_Female_20_to_29_years | 0 | 1 | 0.49 | 0.71 | 0.04 | 0.17 | 0.30 | 0.56 | 5.55 | ▇▁▁▁▁ |
| Other_Female_30_to_39_years | 0 | 1 | 0.48 | 0.75 | 0.04 | 0.15 | 0.28 | 0.52 | 5.36 | ▇▁▁▁▁ |
| Other_Female_40_to_49_years | 0 | 1 | 0.39 | 0.70 | 0.02 | 0.11 | 0.21 | 0.38 | 5.46 | ▇▁▁▁▁ |
| Other_Female_50_to_64_years | 0 | 1 | 0.38 | 0.84 | 0.02 | 0.09 | 0.18 | 0.35 | 7.10 | ▇▁▁▁▁ |
| Other_Female_65_years_and_over | 0 | 1 | 0.25 | 0.72 | 0.01 | 0.04 | 0.09 | 0.18 | 6.20 | ▇▁▁▁▁ |
| Other_Male_10_to_19_years | 0 | 1 | 0.53 | 0.81 | 0.03 | 0.15 | 0.28 | 0.58 | 5.58 | ▇▁▁▁▁ |
| Other_Male_20_to_29_years | 0 | 1 | 0.48 | 0.71 | 0.03 | 0.16 | 0.29 | 0.54 | 5.33 | ▇▁▁▁▁ |
| Other_Male_30_to_39_years | 0 | 1 | 0.44 | 0.71 | 0.03 | 0.14 | 0.26 | 0.48 | 5.06 | ▇▁▁▁▁ |
| Other_Male_40_to_49_years | 0 | 1 | 0.35 | 0.66 | 0.02 | 0.09 | 0.19 | 0.34 | 5.13 | ▇▁▁▁▁ |
| Other_Male_50_to_64_years | 0 | 1 | 0.33 | 0.74 | 0.01 | 0.08 | 0.16 | 0.30 | 6.50 | ▇▁▁▁▁ |
| Other_Male_65_years_and_over | 0 | 1 | 0.19 | 0.59 | 0.01 | 0.03 | 0.07 | 0.14 | 4.51 | ▇▁▁▁▁ |
| White_Female_10_to_19_years | 0 | 1 | 5.69 | 1.37 | 0.94 | 4.96 | 5.79 | 6.57 | 9.45 | ▁▁▇▆▁ |
| White_Female_20_to_29_years | 0 | 1 | 6.07 | 1.36 | 1.59 | 5.23 | 5.90 | 6.93 | 9.65 | ▁▂▇▅▂ |
| White_Female_30_to_39_years | 0 | 1 | 6.15 | 1.22 | 1.53 | 5.45 | 6.28 | 7.00 | 8.95 | ▁▁▅▇▂ |
| White_Female_40_to_49_years | 0 | 1 | 5.56 | 1.22 | 1.20 | 4.84 | 5.66 | 6.39 | 8.33 | ▁▁▇▇▂ |
| White_Female_50_to_64_years | 0 | 1 | 6.55 | 1.45 | 1.72 | 6.00 | 6.57 | 7.32 | 11.40 | ▁▂▇▂▁ |
| White_Female_65_years_and_over | 0 | 1 | 6.40 | 1.71 | 1.05 | 5.37 | 6.67 | 7.54 | 9.90 | ▁▁▆▇▂ |
| White_Male_10_to_19_years | 0 | 1 | 6.00 | 1.42 | 1.02 | 5.26 | 6.11 | 6.91 | 9.74 | ▁▁▇▇▁ |
| White_Male_20_to_29_years | 0 | 1 | 6.26 | 1.32 | 2.41 | 5.42 | 6.10 | 7.13 | 9.96 | ▁▃▇▃▁ |
| White_Male_30_to_39_years | 0 | 1 | 6.25 | 1.18 | 1.93 | 5.57 | 6.31 | 7.04 | 9.67 | ▁▂▇▆▁ |
| White_Male_40_to_49_years | 0 | 1 | 5.56 | 1.21 | 1.35 | 4.77 | 5.66 | 6.40 | 8.24 | ▁▁▇▇▃ |
| White_Male_50_to_64_years | 0 | 1 | 6.23 | 1.39 | 1.78 | 5.62 | 6.16 | 6.92 | 10.93 | ▁▂▇▂▁ |
| White_Male_65_years_and_over | 0 | 1 | 4.56 | 1.19 | 1.02 | 3.80 | 4.78 | 5.34 | 7.51 | ▁▂▇▇▁ |
| Unemployment_rate | 0 | 1 | 6.04 | 2.11 | 2.30 | 4.50 | 5.60 | 7.20 | 17.80 | ▇▇▂▁▁ |
| Poverty_rate | 0 | 1 | 13.39 | 3.86 | 5.70 | 10.40 | 12.80 | 15.60 | 27.20 | ▃▇▅▂▁ |
| Viol_crime_count | 0 | 1 | 32452.11 | 46790.78 | 322.00 | 5598.75 | 14684.00 | 39119.00 | 345624.00 | ▇▁▁▁▁ |
| Population | 0 | 1 | 5559352.78 | 6092703.87 | 404680.00 | 1570224.75 | 3659637.00 | 6487139.00 | 37349363.00 | ▇▂▁▁▁ |
| police_per_100k_lag | 0 | 1 | 315.19 | 116.43 | 83.76 | 247.63 | 298.45 | 354.02 | 1021.14 | ▆▇▁▁▁ |
| RTC_LAW_YEAR | 0 | 1 | Inf | NaN | 1985.00 | 1994.25 | 1997.00 | 2011.25 | Inf | ▇▇▃▅▂ |
| TIME_0 | 0 | 1 | 1980.00 | 0.00 | 1980.00 | 1980.00 | 1980.00 | 1980.00 | 1980.00 | ▁▁▇▁▁ |
| TIME_INF | 0 | 1 | 2010.00 | 0.00 | 2010.00 | 2010.00 | 2010.00 | 2010.00 | 2010.00 | ▁▁▇▁▁ |
| Viol_crime_rate_1k | 0 | 1 | 5.10 | 3.21 | 0.48 | 2.87 | 4.63 | 6.47 | 29.30 | ▇▃▁▁▁ |
| Viol_crime_rate_1k_log | 0 | 1 | 1.46 | 0.60 | -0.74 | 1.05 | 1.53 | 1.87 | 3.38 | ▁▂▇▅▁ |
| Population_log | 0 | 1 | 15.04 | 1.02 | 12.91 | 14.27 | 15.11 | 15.69 | 17.44 | ▃▅▇▅▂ |
Rows: 1,364
Columns: 20
$ YEAR <dbl> 1980, 1980, 1980, 1980, 1980, 1980, 1980, 19…
$ STATE <chr> "Alaska", "Arizona", "Arkansas", "California…
$ Black_Male_15_to_19_years <dbl> 0.16704557, 0.17475437, 0.95451390, 0.433886…
$ Black_Male_20_to_39_years <dbl> 0.99337748, 0.52671209, 1.97382132, 1.353260…
$ Other_Male_15_to_19_years <dbl> 1.12978156, 0.41504620, 0.03849163, 0.312308…
$ Other_Male_20_to_39_years <dbl> 2.96332905, 0.98492602, 0.12425676, 1.213007…
$ White_Male_15_to_19_years <dbl> 3.6278047, 4.0915770, 3.7401985, 3.8358473, …
$ White_Male_20_to_39_years <dbl> 18.288524, 14.692380, 12.125127, 14.990947, …
$ Unemployment_rate <dbl> 9.6, 6.6, 7.6, 6.8, 5.8, 7.6, 7.4, 6.1, 6.3,…
$ Poverty_rate <dbl> 9.6, 12.8, 21.5, 11.0, 8.6, 11.8, 20.9, 16.7…
$ Viol_crime_count <dbl> 1919, 17673, 7656, 210290, 15215, 2824, 1277…
$ Population <dbl> 404680, 2735840, 2288809, 23792840, 2909545,…
$ police_per_100k_lag <dbl> 194.72176, 262.66156, 152.00045, 243.92632, …
$ RTC_LAW_YEAR <dbl> 1995, 1995, 1996, Inf, 2003, Inf, Inf, 1988,…
$ RTC_LAW <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FA…
$ TIME_0 <dbl> 1980, 1980, 1980, 1980, 1980, 1980, 1980, 19…
$ TIME_INF <dbl> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 20…
$ Viol_crime_rate_1k <dbl> 4.742018, 6.459808, 3.344971, 8.838373, 5.22…
$ Viol_crime_rate_1k_log <dbl> 1.5564629, 1.8655995, 1.2074581, 2.1791028, …
$ Population_log <dbl> 12.91085, 14.82195, 14.64354, 16.98490, 14.8…
| Name | DONOHUE_DF |
| Number of rows | 1364 |
| Number of columns | 20 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| logical | 1 |
| numeric | 18 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| STATE | 0 | 1 | 4 | 20 | 0 | 44 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| RTC_LAW | 0 | 1 | 0.36 | FAL: 868, TRU: 496 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| YEAR | 0 | 1 | 1995.00 | 8.95 | 1980.00 | 1987.00 | 1995.00 | 2003.00 | 2010.00 | ▇▇▇▇▇ |
| Black_Male_15_to_19_years | 0 | 1 | 0.53 | 0.51 | 0.02 | 0.15 | 0.36 | 0.74 | 3.46 | ▇▂▁▁▁ |
| Black_Male_20_to_39_years | 0 | 1 | 1.77 | 1.76 | 0.07 | 0.57 | 1.19 | 2.32 | 11.33 | ▇▂▁▁▁ |
| Other_Male_15_to_19_years | 0 | 1 | 0.26 | 0.40 | 0.01 | 0.08 | 0.14 | 0.29 | 2.90 | ▇▁▁▁▁ |
| Other_Male_20_to_39_years | 0 | 1 | 0.93 | 1.42 | 0.07 | 0.31 | 0.55 | 1.01 | 9.90 | ▇▁▁▁▁ |
| White_Male_15_to_19_years | 0 | 1 | 3.07 | 0.72 | 0.55 | 2.67 | 3.13 | 3.52 | 4.99 | ▁▁▇▇▁ |
| White_Male_20_to_39_years | 0 | 1 | 12.51 | 2.28 | 4.41 | 11.13 | 12.61 | 14.13 | 18.29 | ▁▂▇▇▂ |
| Unemployment_rate | 0 | 1 | 6.04 | 2.11 | 2.30 | 4.50 | 5.60 | 7.20 | 17.80 | ▇▇▂▁▁ |
| Poverty_rate | 0 | 1 | 13.39 | 3.86 | 5.70 | 10.40 | 12.80 | 15.60 | 27.20 | ▃▇▅▂▁ |
| Viol_crime_count | 0 | 1 | 32452.11 | 46790.78 | 322.00 | 5598.75 | 14684.00 | 39119.00 | 345624.00 | ▇▁▁▁▁ |
| Population | 0 | 1 | 5559352.78 | 6092703.87 | 404680.00 | 1570224.75 | 3659637.00 | 6487139.00 | 37349363.00 | ▇▂▁▁▁ |
| police_per_100k_lag | 0 | 1 | 315.19 | 116.43 | 83.76 | 247.63 | 298.45 | 354.02 | 1021.14 | ▆▇▁▁▁ |
| RTC_LAW_YEAR | 0 | 1 | Inf | NaN | 1985.00 | 1994.25 | 1997.00 | 2011.25 | Inf | ▇▇▃▅▂ |
| TIME_0 | 0 | 1 | 1980.00 | 0.00 | 1980.00 | 1980.00 | 1980.00 | 1980.00 | 1980.00 | ▁▁▇▁▁ |
| TIME_INF | 0 | 1 | 2010.00 | 0.00 | 2010.00 | 2010.00 | 2010.00 | 2010.00 | 2010.00 | ▁▁▇▁▁ |
| Viol_crime_rate_1k | 0 | 1 | 5.10 | 3.21 | 0.48 | 2.87 | 4.63 | 6.47 | 29.30 | ▇▃▁▁▁ |
| Viol_crime_rate_1k_log | 0 | 1 | 1.46 | 0.60 | -0.74 | 1.05 | 1.53 | 1.87 | 3.38 | ▁▂▇▅▁ |
| Population_log | 0 | 1 | 15.04 | 1.02 | 12.91 | 14.27 | 15.11 | 15.69 | 17.44 | ▃▅▇▅▂ |
DONOHUE_DF |>
group_by(YEAR) |>
summarise(Population = sum(Population)) |>
ggplot(aes(x = YEAR, y = Population)) +
geom_line() +
scale_x_continuous(
breaks = seq(1980, 2010, by = 1),
limits = c(1980, 2010),
labels = c(seq(1980, 2010, by = 1))
) +
labs(
title = "Population has steadily increased",
x = "Year",
y = "Population"
) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90),
plot.title.position = "plot")df |>
ggplot(aes(x = YEAR, y = Viol_crime_rate_100k_log)) +
geom_line() +
scale_x_continuous(
breaks = seq(1980, 2010, by = 1),
limits = c(1980, 2010),
labels = c(seq(1980, 2010, by = 1))
) +
scale_y_continuous(
breaks = seq(5.75, 6.75, by = 0.25),
limits = c(5.75, 6.75)
) +
labs(
title = "Crime rates fluctuate over time",
x = "Year",
y = "ln(violent crimes per 100,000 people)"
) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90),
plot.title.position = "plot")p <- DONOHUE_DF |>
mutate(Viol_crime_rate_100k_log = log((Viol_crime_count * 100000) / Population)) |>
ggplot(aes(x = YEAR, y = Viol_crime_rate_100k_log, color = STATE)) +
geom_point(size = 0.5) +
geom_line(aes(group = STATE),
size = 0.5,
show.legend = FALSE
) +
geom_text_repel(data = DONOHUE_DF |>
mutate(Viol_crime_rate_100k_log = log((Viol_crime_count * 100000) / Population)) |>
filter(YEAR == last(YEAR)),
aes(label = STATE,x = YEAR, y = Viol_crime_rate_100k_log),
size = 3, alpha = 1, nudge_x = 1, direction = "y",
hjust = 1, vjust = 1, segment.size = 0.25, segment.alpha = 0.25,
force = 1, max.iter = 9999)p +
guides(color = "none") +
scale_x_continuous(
breaks = seq(1980, 2015, by = 1),
limits = c(1980, 2015),
labels = c(seq(1980, 2010, by = 1), rep("", 5))
) +
scale_y_continuous(
breaks = seq(3.5, 8.5, by = 0.5),
limits = c(3.5, 8.5)
) +
labs(
title = "States have different levels of crime",
x = "Year", y = "ln(violent crimes per 100,000 people)"
) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90), plot.title.position = "plot")DONOHUE_DF |>
group_by(YEAR) |>
summarise(Police = sum(police_per_100k_lag)) |>
ggplot(aes(x = YEAR, y = Police)) +
geom_line() +
scale_x_continuous(
breaks = seq(1980, 2010, by = 1),
limits = c(1980, 2010),
labels = c(seq(1980, 2010, by = 1))
) +
labs(
title = "Police Presence has increased over time with fluctuations",
x = "Year",
y = "Police Presence per 100K people"
) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90),
plot.title.position = "plot")🧠 Consider the data we’re working with and our questions of interest, what would you like to know that you don’t know yet?
❗ Do some EDA! Try to learn something from the data that we haven’t yet discussed. (Summarize data, make a plot, make a table, etc.)